One of the most important arguments in favor of deep learning is the quality of its results. In image recognition and speech processing, in particular, the technology is clearly superior to all others. Provided with high-quality training data, deep learning can carry out routine work much more efficiently and much faster than any human – without any signs of fatigue either, and with no change in quality.
With other forms of machine learning, developers analyze the raw data and periodically define additional features that the algorithm is to take into account while learning in order to improve the AI’s forecasting power. With deep learning, the system itself recognizes useful variables and incorporates these into its learning process. After the initial training period it can learn without any human guidance, saving both time and money since skilled employees aren't necessary for future development.
Up to now, large quantities of data had to be labeled manually in order to make machine learning possible. In image recognition, for example, employees were required who would assign the label dog or cat to the images. With deep learning, the manual training period is significantly shorter. Above all this is relevant because, while general corporate practice certainly does involve collecting large quantities of data, only in rare cases does it exist in the form of structured data (telephone numbers, address, credit cards, etc.). In most cases it is stored as unstructured data (images, documents, emails, etc.). Unlike alternative methods of machine learning, deep learning can evaluate different sources of unstructured data while considering the task at hand.
The argument that the technology is too costly in practice for it to be applicable on a large scale is losing traction. Services like Google Visionor IBM Watson are increasingly emerging, allowing companies to build on existing neural networks instead of having to develop them from scratch. With this, in the future deep learning will be more and more capable of playing on its strengths in corporate practice.